ArticlePLoS computational biology2019
Prediction of ultra-high-order antibiotic combinations based on pairwise interactions.
Article in PLoS computational biology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 papers.
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Who cites it
42 citing papers in PubMed, 71 citations in OpenAlex.
- Landscape transcriptomics reveals ecological constraints on fish under anthropogenic coastal change.Nature communications · 2026Article
- TACTIC: A transfer learning framework to predict drug interactions in emerging pathogens.Cell reports methods · 2026Article
- Large-scale testing of antimicrobial lethality at single-cell resolution predicts mycobacterial infection outcomes.Nature microbiology · 2026Article
- Artificial intelligence applications in oxaliplatin-based chemotherapy for colon cancer: advancing prognosis, toxicity prediction, and dose personalization.Frontiers in pharmacology · 2026Review
- Experimental ecology and the balance between realism and feasibility in aquatic ecosystems.Nature communications · 2025Review
- Interactions within higher-order antibiotic combinations do not influence the rate of adaptation in bacteria.Evolution; international journal of organic evolution · 2025Article
- Chemical genetic interactions elucidate pathways controlling tuberculosis antibiotic efficacy during infection.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- GEODE: anFrontiers in pharmacology · 2025Article
- Drug combinations targeting antibiotic resistance.npj antimicrobials and resistance · 2024Review
- Number and dissimilarity of global change factors influences soil properties and functions.Nature communications · 2024Article
- The many dimensions of combination therapy: How to combine antibiotics to limit resistance evolution.Evolutionary applications · 2024Article
- Transfer learning predicts species-specific drug interactions in emerging pathogens.bioRxiv : the preprint server for biology · 2024Article
- Use of multiple pharmacodynamic measures to deconstruct the Nix-TB regimen in a short-course murine model of tuberculosis.Antimicrobial agents and chemotherapy · 2024Article
- A continuous epistasis model for predicting growth rate given combinatorial variation in gene expression and environment.Cell systems · 2024Article
- The evolution of resistance to synergistic multi-drug combinations is more complex than evolving resistance to each individual drug component.Evolutionary applications · 2023Article
- The pharmacokinetic-pharmacodynamic modelling framework as a tool to predict drug resistance evolution.Microbiology (Reading, England) · 2023Article
- A demographic approach for predicting population responses to multifactorial stressors.AoB PLANTS · 2023Review
- Increasing the number of stressors reduces soil ecosystem services worldwide.Nature climate change · 2023Article
- Article
- Synergistic effects of complex drug combinations in colorectal cancer cells predicted by logical modelling.Frontiers in systems biology · 2023Article
Corrections and comments
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Authors and funding
4 authors at 3 institutions in 2 countries.
Funding
Abstract
Drug combinations are a promising approach to achieve high efficacy at low doses and to overcome resistance. Drug combinations are especially useful when drugs cannot achieve effectiveness at tolerable doses, as occurs in cancer and tuberculosis (TB). However, discovery of effective drug combinations faces the challenge of combinatorial explosion, in which the number of possible combinations increases exponentially with the number of drugs and doses. A recent advance, called the dose model, uses a mathematical formula to overcome combinatorial explosion by reducing the problem to a feasible quadratic one: using data on drug pairs at a few doses, the dose model accurately predicts the effect of combinations of three and four drugs at all doses. The dose model has not yet been tested on higher-order combinations beyond four drugs. To address this, we measured the effect of combinations of up to ten antibiotics on E. coli growth, and of up to five tuberculosis (TB) drugs on the growth of M. tuberculosis. We find that the dose model accurately predicts the effect of these higher-order combinations, including cases of strong synergy and antagonism. This study supports the view that the interactions between drug pairs carries key information that largely determines higher-order interactions. Therefore, systematic study of pairwise drug interactions is a compelling strategy to prioritize drug regimens in high-dimensional spaces.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.